Characterization of petroleum using near-infrared spectroscopy: Quantitative modeling for the true boiling point curve and specific gravity

This work describes a new approach to predict the true boiling point (TBP) curve and to estimate the API gravity in order to characterize the petroleum processed in refineries by using the information present in its absorbance spectrum obtained in the near-infrared region (NIR). The absorbance spectra were obtained in the range from 3700 to 10000 cm−1 employing a CaF2 transmittance cell with a 0.5 mm light path. Three spectral regions were evaluated for modeling purpose: 5000–3900 cm−1, 6000–3700 cm−1, and 9000–700 cm−1. The spectral region corresponding to the combination of C–H vibrations produces absorption spectra with very good quality while the region above 6500 cm−1 is dominated by scattering of the radiation. The absorbance spectra of a total of 122 samples of petroleum and petroleum blends coming from various producing regions in Brazil and abroad were obtained and pre-processed to correct for base line shift and for the integrated area. Two approaches were employed to obtain the models: one using artificial neural networks (ANN) and the other using partial least squares (PLS). The results showed that PLS gives better predictions than ANN for the API gravity and the TBP curve. The best results were obtained using the 5000–3900 cm−1 spectral range. In an external validation, the average RMSEP for the volume of distillate along the TBP curve employing PLS model was 1.13V% while that for API gravity was 0.24. A comparison between the results obtained by a simulator used by the refinery and the PLS model revealed a better performance for the model based on NIR spectrometry.